We present a control approach for autonomous vehicles based on deep reinforcement
learning. A neural network agent is trained to map its estimated state to acceleration and …

In this article, we explore the feasibility of applying proximal policy optimization, a state-of-
the-art deep reinforcement learning algorithm for continuous control tasks, on the dual …

This paper presents a predictive controller for longitudinal motion of automated vehicles
based on Deep Reinforcement Learning. It uses advance information about future speed …

The operational space of an autonomous vehicle (AV) can be diverse and vary significantly.
Due to this, formulating a rule based decision maker for selecting driving maneuvers may …

Most methods that attempt to tackle the problem of Autonomous Driving and overtaking
usually try to either directly minimize an objective function or iteratively in a Reinforcement …

Traditional planning and control methods could fail to find a feasible trajectory for an
autonomous vehicle to execute amongst dense traffic on roads. This is because the obstacle …

In many real-world decision making problems, reaching an optimal decision requires taking
into account a variable number of objects around the agent. Autonomous driving is a domain …

Reinforcement learning is considered to be a strong AI paradigm which can be used to
teach machines through interaction with the environment and learning from their mistakes …

Reinforcement learning (RL) is attracting increasing interests in autonomous driving due to
its potential to solve complex classification and control problems. However, existing RL …

Academic research in the field of autonomous vehicles has reached high popularity in
recent years related to several topics as sensor technologies, V2X communications, safety …